Shallow subsurface heat recycling is a sustainable global space heating alternative

Despite the global interest in green energy alternatives, little attention has focused on the large-scale viability of recycling the ground heat accumulated due to urbanization, industrialization and climate change. Here we show this theoretical heat potential at a multi-continental scale by first leveraging datasets of groundwater temperature and lithology to assess the distribution of subsurface thermal pollution. We then evaluate subsurface heat recycling for three scenarios: a status quo scenario representing present-day accumulated heat, a recycled scenario with ground temperatures returned to background values, and a climate change scenario representing projected warming impacts. Our analyses reveal that over 50% of sites show recyclable underground heat pollution in the status quo, 25% of locations would be feasible for long-term heat recycling for the recycled scenario, and at least 83% for the climate change scenario. Results highlight that subsurface heat recycling warrants consideration in the move to a low-carbon economy in a warmer world.


Supplementary Notes: Details of data availability
We make all data used in this study available in the Scholars Portal Dataverse under https: //doi.org/10.5683/SP3/2UTTVQ (1). This includes a shapefile and 1 km × 1 km image (.tif) of our classification results (Fig. 5). For those pixels or shapes, values (labels) of 0 indicate class 0: not feasible; values of 1 indicate class 1: potentially feasible; and values of 2 indicate class 2: likely feasible. For the image all other pixels have the value -1. Shared data also includes a table (Data.csv) containing all relevant input variables and results. Each row represents one groundwater measurement location. Each column represents a variable as described below: • ID: Identifies each location. First two letters indicate country, the following symbols the original ID given for this location.
• cV min: Estimated min heat capacity of the aquifer in M Jm −3 K −1 .
• cV median: Estimated median heat capacity of the aquifer in M Jm −3 K −1 .
• cV max: Estimated max heat capacity of the aquifer in M Jm −3 K −1 . • rho bld: estimated building density in percent.
• lambda min: Estimated min thermal conductivity of the unsaturated zone in W m −1 K −1 .
• lambda max: Estimated max thermal conductivity in W m −1 K −1 .
• GST min: Min ground surface temperature in°C.
• GST median: Median ground surface temperature in°C.
• GST max: Max ground surface temperature in°C.
• GWTable min: Min depth to the groundwater table in m.
• GWTable median: Median depth to the groundwater table in m.
• GWTable max: Max depth to the groundwater table in m. • GST rcp45 min: Min ground surface temperature at the end of the century following RCP4.5 in°C.
• GST rcp45 median: Median ground surface temperature at the end of the century following RCP4.5 in°C.
• GST rcp45 max: Max ground surface temperature at the end of the century following RCP4.5 in°C.
• GST rcp85 min: Min ground surface temperature at the end of the century following RCP8.5 in°C.
• GST rcp85 median: Median ground surface temperature at the end of the century following RCP8.5 in°C.
• GST rcp85 max: Max ground surface temperature at the end of the century following RCP8.5 in°C. • HDD min: Min heating degree days based in°C.
• HDD median: Median heating degree days based in°C.
• HDD max: Max heating degree days based in°C.
• HDD rcp45 min: Min heating degree days at the end of the century following RCP4.5 based in°C.
• HDD rcp45 median: Median heating degree days at the end of the century following RCP4.5 based in°C.
• HDD rcp45 max: Max heating degree days at the end of the century following RCP4.5 based in°C.
• HDD rcp85 min: Min heating degree days at the end of the century following RCP8.5 based in°C.
• HDD rcp85 median: Median heating degree days at the end of the century following RCP8.5 based in°C.
• HDD rcp85 max: Max heating degree days at the end of the century following RCP8.5 based in°C.
• pop SSP2: Population at the end of the century following SSP2 in km −2 .
• pop SSP5: Population at the end of the century following SSP5 in km −2 .
• Qdemand rcp45 min: Min heating demand at the end of the century following RCP4.5 and SSP2 in M Jm −2 .
• Qdemand rcp45 median: Median heating demand at the end of the century following RCP4.5 and SSP2 in M Jm −2 .
• Qdemand rcp45 max: Max heating demand at the end of the century following RCP4.5 and SSP2 in M Jm −2 .
• Qdemand rcp85 min: Min heating demand at the end of the century following RCP8.5 and SSP5 in M Jm −2 .
• Qdemand rcp85 median: Median heating demand at the end of the century following RCP8.5 and SSP5 in M Jm −2 .
• Qdemand rcp85 max: Max heating demand at the end of the century following RCP8.5 and SSP5 in M Jm −2 .

Supplementary Figures
Supplementary Figure 1: Contribution-to-variance of input variables and results. Spearman correlation coefficients between variables given on the left and top. For the accumulated heat of scenario stats quo we analysed the correlation to ∆T and heat capacity c V ; for the annual heat exchange in the stats quo we analysed the correlation to GWTs, the thermal conductivity λ, building density ρ Bld (eq. 3), ground surface temperatures T Surf , and groundwater table depth D GW ; Lastly for the ratio of generated heat (scenario recycled ) and heating demands we show the correlation to GDP, heat intensity, population, GW T recycled (eq. 7), the thermal conductivity λ, building density ρ Bld (eq. 3), ground surface temperatures T Surf , and groundwater table depth D GW .
Supplementary  Table 4 for other fit options.
Supplementary Figure 9: Distribution of variables in training data and pixels to be classified. Cumulative histograms of all variables used for the random forest classification for all European locations with unconsolidated sediments (orange line), the excerpt of those used to train the random forest classifier (red line), and all pixels on which the classifier has been applied to (black line).

Supplementary Tables
Supplementary Table 1: Assumed values for heat capacity c V of the aquifer and thermal conductivity λ of the unsaturated zone (where applicable) for our median (min; max) calculations. Values are based on the VDI 4640 (8). Soil types marked with a * indicated that extraction of heat may not be possible due to technical constrains.